The term “Security Constrained Economic Dispatch” or SCED refers to the process of determining a dispatch solution for generation resources within a power transmission network or system, subject to network constraints such as generation resource constraints, network node voltage and/or phase constraints, transmission line current limits, etc. As a general proposition, SCED determines the lowest-cost dispatch solution while conforming the solution to all applicable constraints and while meeting current or projected demands.
The dispatch solution includes values or settings for some or all of the generation sources within the network, and the SCED process may be run on demand and/or on timed interval basis, e.g., every five or ten minutes. In this manner, the dispatch solution is updated in view of the relevant variables, including changing network conditions regarding demand and/or generation, or in view of changing demand projections.
In an example approach, a network operator maintains or otherwise uses a computer system that is configured to determined dispatch solutions in the SCED context, e.g., based on the computer system gathering, receiving or otherwise having access to the various real-time, historic, and projected values for the subject power system. In a particular example of SCED processing, linear programming provides the mechanism for determining the dispatch solution. See B. Stott, J. L. Marinho, “Linear Programming for Power-System Network Security,” IEEE Transactions on Power Apparatus and Systems, pp. 837-848, Vol. PAS-98, No. 3 May/June 1979 (hereafter “Stott”).
As those of ordinary skill in the art will appreciate, a linear programming (LP) model includes an objective function to be minimized or maximized, subject to a number of linear inequalities or constraints. Thus, in one sense, LP may be understood as a mathematical method used to determine an optimal allocation of limited resources to competing activities, where the allocation problem is expressed as a linear objective function and corresponding linear inequality constraints. In the power network context, LP optimizes generation values for the power generation sources in the network, in conformance with power demands (customer loading), and various network operating constraints or limits.
Determining constraint conformance generally involves carrying out a power flow analysis (which can be an approximation) for the power network, as a basis for determining voltages, phases, and current flows within the power network. Power flow analysis therefore provides a mechanism for determining a dispatch solution that conforms to the applicable operating constraints and, more broadly, provides a basis for ensuring conformance to the applicable network operating constraints or limits. Power flow analysis the power system context thus forms the foundation of contingency analysis and the implementation of real-time or other dynamic monitoring systems, including SCED systems of interest herein.
Conventional power flow analysis methodologies typically involve determining element values for passive network components, determining locations and values of all complex power loads, determining generation specifications and constraints, and developing or otherwise using a mathematical model describing power flow in the subject network. The power flow analysis procedure then solves for the voltage profile of the network, solves for the power flows and losses in the network, and checks for constraint violations.
Power flow analysis and, in general, SCED processing becomes even more complex for hybrid AC and high voltage direct current (HVDC) systems, e.g., where the DC system is meshed. While there are known approaches for determining power flows in hybrid networks, known approaches include a number of issues or disadvantages.
For example, the power flow solution may depend on detailed DC grid modeling, which exposes proprietary modeling information. In other instances, processing is incomplete, e.g., one or more of the following items are not considered in the model: converter losses associated with voltage conversion between the AC and DC power systems or grids; control laws; grounding schemes, and bipole configurations. In still other instances, key elements are simplified, e.g., only two-terminal DC links are considered, etc. Other approaches require a much more complex simultaneous optimization of the AC and DC grids involved in the hybrid system. Sequential Gradient Restoration Algorithms (SGRA) and Genetic Algorithms are example approaches to the simultaneous grid solution problem.